论文标题
使用卷积结构对磁共振成像的脑肿瘤进行分割
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture
论文作者
论文摘要
大脑是控制认知过程和身体功能的复杂器官。大脑中的肿瘤是影响大脑正常功能和过程的加速细胞生长。 MRI扫描提供了人体诊断脑肿瘤的最常见测试之一。通过磁共振成像分割脑肿瘤的过程可以为诊断，治疗计划和结果预测提供宝贵的指南。在这里，我们考虑了使用深度学习结构用于肿瘤分割的问题脑肿瘤分割。尽管所提出的体系结构在计算上很容易训练，但它能够达到0.95美元的$ $ $ $。
The brain is a complex organ controlling cognitive process and physical functions. Tumors in the brain are accelerated cell growths affecting the normal function and processes in the brain. MRI scans provides detailed images of the body being one of the most common tests to diagnose brain tumors. The process of segmentation of brain tumors from magnetic resonance imaging can provide a valuable guide for diagnosis, treatment planning and prediction of results. Here we consider the problem brain tumor segmentation using a Deep learning architecture for use in tumor segmentation. Although the proposed architecture is simple and computationally easy to train, it is capable of reaching $IoU$ levels of 0.95.
